Continual Panoptic Perception: Towards Multi-modal Incremental Interpretation of Remote Sensing Images

TL;DR

This paper introduces a continual learning model for remote sensing images that integrates multi-task perception, cross-modal feature extraction, and knowledge distillation to improve interpretation accuracy and reduce forgetting.

Contribution

It proposes a unified multi-modal continual learning framework with a collaborative encoder and task-interactive knowledge distillation for remote sensing.

Findings

Over 13% improvement in panoptic quality with joint optimization

Effective mitigation of catastrophic forgetting in multi-task remote sensing

Validated on a fine-grained panoptic perception dataset

Abstract

Continual learning (CL) breaks off the one-way training manner and enables a model to adapt to new data, semantics and tasks continuously. However, current CL methods mainly focus on single tasks. Besides, CL models are plagued by catastrophic forgetting and semantic drift since the lack of old data, which often occurs in remote-sensing interpretation due to the intricate fine-grained semantics. In this paper, we propose Continual Panoptic Perception (CPP), a unified continual learning model that leverages multi-task joint learning covering pixel-level classification, instance-level segmentation and image-level perception for universal interpretation in remote sensing images. Concretely, we propose a collaborative cross-modal encoder (CCE) to extract the input image features, which supports pixel classification and caption generation synchronously. To inherit the knowledge from the old model without exemplar memory, we propose a task-interactive knowledge distillation (TKD) method, which leverages cross-modal optimization and task-asymmetric pseudo-labeling (TPL) to alleviate catastrophic forgetting. Furthermore, we also propose a joint optimization mechanism to achieve end-to-end multi-modal panoptic perception. Experimental results on the fine-grained panoptic perception dataset validate the effectiveness of the proposed model, and also prove that joint optimization can boost sub-task CL efficiency with over 13\% relative improvement on panoptic quality.